IdeaGraph plus: A Topic-Based Algorithm for Perceiving Unnoticed Events

ISCAS OpenIR

	IdeaGraph plus: A Topic-Based Algorithm for Perceiving Unnoticed Events
	Zhang, Chen; Wang, Hao; Xu, Fanjiang; Hu, Xiaohui
	2013
会议名称	IEEE 13th International Conference on Data Mining (ICDM)
页码	735-741
会议日期	DEC 07-10, 2013
会议地点	Dallas, TX
收录类别	CPCI
出版地	IEEE
ISSN	1550-4786
ISBN	978-0-7695-5109-8
部门归属	[Zhang, Chen; Wang, Hao; Xu, Fanjiang; Hu, Xiaohui] Chinese Acad Sci, State Key Lab Comp Sci, Inst Software, Beijing 100190, Peoples R China.
摘要	In the last few years, chance discovery as an extension of data mining has been proposed to capture rare but significant chances from a single document data for human decision making. Key Graph is a useful miner algorithm as well as a tool to discover chance candidates. On base of that, Idea Graph extended the concept of a chance to uncover more valuable chances. However, Key Graph and Idea Graph both fail to consider semantic relations among terms. In this paper, we propose an improved algorithm called Idea Graph plus which makes use of semantic information to enhance the performance of scenario construction using LDA topic model. Additionally, the term overlaps between sub-scenarios provide a thinking space for human to perceive unnoticed chances. An experiment demonstrates the superiority of Idea Graph plus by comparing with IdeaGraph.; In the last few years, chance discovery as an extension of data mining has been proposed to capture rare but significant chances from a single document data for human decision making. Key Graph is a useful miner algorithm as well as a tool to discover chance candidates. On base of that, Idea Graph extended the concept of a chance to uncover more valuable chances. However, Key Graph and Idea Graph both fail to consider semantic relations among terms. In this paper, we propose an improved algorithm called Idea Graph plus which makes use of semantic information to enhance the performance of scenario construction using LDA topic model. Additionally, the term overlaps between sub-scenarios provide a thinking space for human to perceive unnoticed chances. An experiment demonstrates the superiority of Idea Graph plus by comparing with IdeaGraph.
关键词	Chance Discovery Knowledge Discovery Topic Model Idea Graph Plus Latent Information
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16504
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Zhang, Chen,Wang, Hao,Xu, Fanjiang,et al. IdeaGraph plus: A Topic-Based Algorithm for Perceiving Unnoticed Events[C]. IEEE,2013:735-741.